zero_mean_unit_obs_sd_lm(torch.tensor([10.0, 10.0]))[0],
torch.tensor([[100.0, -100.0]]),
),
(
group_linear_model(
torch.tensor(0.0),
torch.tensor([10.0]),
torch.tensor(0.0),
torch.tensor([10.0]),
torch.tensor(1.0),
),
lm_2p_10_10_1_w12,
torch.tensor([[-1.5, 0.5], [1.5, 0.0]]),
),
(
known_covariance_linear_model(torch.tensor(
[1.0, -1.0]), torch.tensor([10.0, 10.0]), torch.tensor(1.0)),
nz_lm_2p_10_10_1,
torch.tensor([[-1.0, 0.5], [2.5, -2.0]]),
),
(
normal_inverse_gamma_linear_model(
torch.tensor([1.0, -1.0]),
torch.tensor(0.1),
torch.tensor(2.0),
torch.tensor(2.0),
),
normal_inv_gamma_2_2_10_10,
torch.tensor([[1.0, -0.5], [1.5, 2.0]]),
),
(
logistic_regression_model(torch.tensor([1.0, -1.0]),
]
response_dist = dist.TransformedDistribution(base_dist, transforms)
y = pyro.sample("y", response_dist)
return y
@pytest.mark.parametrize(
"model1,model2,design",
[(zero_mean_unit_obs_sd_lm(torch.tensor(
[10., 10.]))[0], lm_2p_10_10_1, torch.tensor([[1., -1.]])),
(lm_2p_10_10_1, zero_mean_unit_obs_sd_lm(torch.tensor(
[10., 10.]))[0], torch.tensor([[100., -100.]])),
(group_linear_model(torch.tensor(0.), torch.tensor(
[10.]), torch.tensor(0.), torch.tensor([10.]), torch.tensor(1.)),
lm_2p_10_10_1_w12, torch.tensor([[-1.5, 0.5], [1.5, 0.]])),
(known_covariance_linear_model(torch.tensor(
[1., -1.]), torch.tensor([10., 10.]), torch.tensor(1.)),
nz_lm_2p_10_10_1, torch.tensor([[-1., 0.5], [2.5, -2.]])),
(normal_inverse_gamma_linear_model(torch.tensor(
[1., -1.]), torch.tensor(.1), torch.tensor(2.), torch.tensor(2.)),
normal_inv_gamma_2_2_10_10, torch.tensor([[1., -0.5], [1.5, 2.]])),
(logistic_regression_model(torch.tensor([1., -1.]), torch.tensor(10.)),
lr_10_10, torch.tensor([[6., -1.5], [.5, 0.]])),
(sigmoid_model(torch.tensor([1., -1.]), torch.tensor([10., 10.]),
torch.tensor(0.), torch.tensor([1., 1.]), torch.tensor(1.),
torch.tensor(2.), torch.tensor(2.),
torch.eye(2)), sigmoid_example,
torch.cat([torch.tensor([[1., 1.], [.5, -2.5]]),
torch.eye(2)], dim=-1))])
def test_log_prob_matches(model1, model2, design):
trace = poutine.trace(model1).get_trace(design)
trace.compute_log_prob()
def linear_model():
return known_covariance_linear_model(coef_means=torch.tensor(0.),
coef_sds=torch.tensor([1., 1.5]),
observation_sd=torch.tensor(1.))